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Tech Stack
Tools & technologiesAirflowAWSAzureBigQueryCloudGoogle Cloud PlatformKafkaKubernetesNoSQLNumpyPandasPythonScikit-LearnSparkSQL
About the role
Key responsibilities & impact- Architect and design machine learning systems capable of processing millions of real-time data points, leveraging feature stores, real-time inference pipelines, and scalable model serving frameworks to ensure high performance and low latency
- Drive architectural decision-making for data and ML systems through RFC processes, ensuring solutions are scalable, statistically sound, and future-proof
- Contribute hands-on to data and ML challenges, including building data architectures and high-performance feature engineering pipelines
- Collaborate closely with DevOps teams to ensure ML infrastructure (Kubernetes, cloud platforms, GPU clusters) is optimized for training and inference workloads
- Define and evolve scalable data architectures that support advanced analytics, predictive modeling, and business growth
- Mentor and guide Senior Data Scientists and ML Engineers, fostering strong practices in statistical rigor, MLOps, and systems thinking
- Support other tasks or projects as assigned to meet team and business needs
Requirements
What you’ll need- Degree in a STEM field such as Computer Science, Engineering, or Applied Mathematics, or equivalent practical experience
- 5+ years of combined experience across Data Engineering, Data Architecture, and Data Science
- Proven experience designing and deploying large-scale, distributed data systems handling high transaction volumes
- Strong expertise in cloud environments such as AWS, GCP, or Azure, and modern data platforms such as Snowflake, Databricks, or BigQuery
- Solid understanding of data modeling principles, including relational, dimensional, and NoSQL approaches
- Experience building and orchestrating data pipelines using tools such as Airflow, dbt, Spark, or Kafka
- Knowledge of data governance, security, and compliance best practices
- Advanced proficiency in Python and SQL, with experience using data science libraries such as pandas, NumPy, and scikit-learn
- Proven track record of building, training, and deploying machine learning models to solve real-world business problems
- Experience applying MLOps principles to move models from experimentation to production-ready systems
- Experience developing billing and rating systems for AI-driven or consumption-based models would be considered an advantage
- Hands-on experience integrating AI services or building advanced AI solutions such as RAG pipelines or API-based AI workflows would be considered an advantage
Benefits
Comp & perks- Work from anywhere - this is a remote opportunity with a primary focus on candidates based in the EU due to team needs and coverage
- A competitive salary that values you and your unique skill sets
- Career advancement & professional development opportunities to help you reach your full potential
- Flexible work arrangements to support work/life balance
ATS Keywords
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Hard Skills & Tools
machine learningdata architecturedata engineeringdata sciencedata modelingMLOpsfeature engineeringPythonSQLcloud environments
Soft Skills
mentoringcollaborationarchitectural decision-makingstatistical rigorsystems thinking